Assessing exposure to food and beverage advertisements surrounding schools in Vancouver, BC

Health and Place 58 (2019) 102066

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Health & Place journal homepage: www.elsevier.com/locate/healthplace

Assessing exposure to food and beverage advertisements surrounding schools in Vancouver, BC

T

Cayley E. Velazqueza,c, , Madeleine I.G. Daeppb, Jennifer L. Blacka ⁎

a

Food, Nutrition and Health, Faculty of Land & Food Systems, University of British Columbia, Vancouver, BC, Canada V6T 1Z4 Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge 02139, MA, USA c Department of Biology, Kwantlen Polytechnic University, Surrey, BC, Canada V3W 2M8 b

ARTICLE INFO

ABSTRACT

Keywords: Advertising Food Beverage Food advertising Food environment Schools

Recent policy initiatives call for restricting food marketing to children, yet little is known about children's current exposure to outdoor advertisements. This paper describes the prevalence and characteristics of food- or beverage-related advertisements surrounding 25 public elementary and secondary schools in Vancouver, Canada and assesses whether the informational food environment differs by neighbourhood or school characteristics. All but four schools had at least one food- or beverage-related advertisement within 400 m (median: 18, range: 0–96) and approximately 90% of food or beverage advertisements were for items not recommended for frequent consumption by provincial school food guidelines. After controlling for commercial density, secondary schools were associated with more outdoor food and beverage advertisements overall in comparison with elementary schools. The presence of an additional limited-service food outlet within 400 m was associated with a 7% increase in the number of overall advertisements (p < 0.001) while an additional grocery store was associated with fewer advertisements (IRR: 0.69, p < 0.001), controlling for commercial density. Findings suggest the need to consider the informational food environment as part of broader assessments of the school and retail food environments.

1. Introduction Childhood and adolescence are frequently recognized as critical periods for the development of healthy eating habits that can track into adulthood (Birch and Fisher, 1998; Craigie et al., 2011; Kelder et al., 1994), yet youth from high-income countries frequently do not meet national dietary recommendations (Black and Billette, 2013; Garriguet, 2007; Kim et al., 2014; Muñoz et al., 1997; Savige et al., 2007; Veugelers and Fitzgerald, 2005). Although the determinants of dietary intake are complex, recent evidence indicates that features of the food environment may shape children's dietary practices above and beyond individual and family-level factors (Engler-Stringer et al., 2014a; Jennings et al., 2011; Lamichhane et al., 2012; Seliske et al., 2013; Van Hulst et al., 2014). The food environment has been conceptualized by Glanz et al. (2005) as including four aspects: (1) the community nutrition environment (e.g., type and location of food outlets); (2) the consumer nutrition environment (e.g., availability of healthy food options); (3) the organizational nutrition environment (e.g., food access in settings such as schools) and; (4) the information environment (e.g., food-related advertising and logos).

⁎

Assessments of the community, consumer, and organizational nutrition environments in and around schools are increasingly common in the academic literature (Morland, 2015; Engler-Stringer et al., 2014b; Williams et al., 2014), yet most have overlooked the information environment. Food marketing, included as part of the information environment, commonly depicts minimally nutritious items such as soft drinks and fast food (Bell et al., 2009a, 2009b; Gantz et al., 2007; Health Canada, 2017a; Powell et al., 2011). Moreover, exposure to marketing has been associated with the food preferences, requests, choices, and weight-related outcomes of youth across various age ranges (Hastings et al., 2003; Institute of Medicine, 2006). Still the bulk of literature on food marketing has measured exposure through particular channels mainly focused on television, but little work has assessed the role of specific settings including schools where youth spend much of their time. North American studies, primarily from the United States, suggest that students are exposed to a broad array of food marketing strategies on school grounds, and that generally, high school students see more food advertisements than elementary school students (Johnston et al., 2007; Terry-McElrath et al., 2014; Velazquez et al., 2015b, 2017). Yet

Corresponding author at: Department of Biology, Kwantlen Polytechnic University, Surrey, BC, Canada V3W 2M8. E-mail addresses: [email protected] (C.E. Velazquez), [email protected] (M.I.G. Daepp), [email protected] (J.L. Black).

https://doi.org/10.1016/j.healthplace.2018.12.007 Received 14 May 2018; Received in revised form 17 November 2018; Accepted 12 December 2018 Available online 11 January 2019 1353-8292/ © 2018 Elsevier Ltd. All rights reserved.

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schools are located within communities and students must move between their homes and school on a daily basis. While little research has specifically examined the extent to which exposure around schools contributes to overall marketing exposure, consideration of the information environment around schools is warranted given that students spend a considerable amount of time in and around school each day. In Vancouver, Canada, where one survey found that 55.8% of 5th–8th grade students report walking as a usual mode of transportation to school,1 a majority of children are potentially exposed to billboards and posters twice a day, every school day. Knowing more about this potentially regular and recurring source of exposure to food and beverage products could help improve current understanding of the influence that food marketing around schools has on children's food preferences and diet-related outcomes. A small group of studies from high-income countries have examined the spatial distribution of outdoor food marketing (Adams et al., 2011; Cassady et al., 2015; Hillier et al., 2009; Isgor et al., 2016; Lesser et al., 2013; Lowery and Sloane, 2014; Yancey et al., 2009). Evidence to date suggests that children's exposure to food marketing varies depending on the racial/ethnic and socio-economic composition of the geographic areas under study. In the United States (Sacramento County, CA), Cassady et al. (2015) found that the density of advertisements for unhealthy beverages (defined as high-calorie, minimally nutritious beverages like soft drinks) was greatest in low-income Latino and AfricanAmerican neighbourhoods compared to both high- and low-income predominantly White neighbourhoods. Several other American studies have similarly reported more outdoor food advertisements available in ethnic minority neighbourhoods (Lesser et al., 2013; Lowery and Sloane, 2014; Yancey et al., 2009). Moreover, using data from a national sample of retail food stores in the US, Isgor et al. (2016) found that the prevalence of all food and beverage advertisements, including those for minimally nutritious products such as regular soda, was highest in low-income communities. Together, these findings suggest that outdoor advertising may disproportionately be targeting ethnic minority groups and/or individuals living in lower income neighbourhoods. Few studies have examined outdoor food and beverage advertising specifically around schools (Kelly et al., 2008, 2015; Maher et al., 2005; Herrera and Pasch, 2018). Kelly and colleagues (2008) examined a 500 m radius around 40 primary schools in Australia and found that 80% of food advertisements were for minimally nutritious items (e.g., soft drinks, ice cream). Kelly et al. (2015) conducted a similar study to examine the density of food and beverage advertisements around 30 schools in both Mongolia and The Philippines and found that food advertisements were clustered in the area nearest to schools. Further, the authors found that ≥ 85% of food advertisements were for unhealthy food or beverage items, with soft drinks being the most frequently depicted products. Maher et al. (2005) reported similar findings regarding type of products advertised around New Zealand schools. Generally, studies have found that advertisements cluster around schools and that most items promoted depict unhealthy food and beverage products not consistent with nutritional guidelines (Kelly et al., 2008, 2015; Maher et al., 2005). Describing the outdoor food and beverage advertising environment around schools has contributed to understanding what children and adolescents are exposed to, but gaps remain. To date, no published research has examined outdoor food and beverage advertising in Canada, nor in areas surrounding child-focused institutions such as schools. While studies in other countries have examined whether exposures around schools differ by racial/ethnic composition (Herrera and Pasch, 2018) or socio-economic status (Kelly et al., 2008; Maher

et al., 2005), results around schools suggest that study context and potentially confounding characteristics of the school environment might impact findings For example, Kelly et al. (2008) found proportionally fewer advertisements for “non-core” (unhealthy) foods or beverages in high- versus low-SES neighbourhoods surrounding schools in Australia, but Maher et al. (2005) found significantly more ‘unhealthy’ food advertisements in high-SES versus low-SES neighbourhoods surrounding New Zealand schools. One potential explanation for differences in exposure and correlates of advertising exposure around schools found across studies is that commercial density confounds the relationship between neighbourhood socioeconomic status and advertising exposures. Food and beverage companies provide promotional materials to the shops selling their products (Kelly et al., 2008), and such retailers tend to be more prevalent in low socio-economic status versus high socio-economic status neighbourhoods (Black and Day, 2012; Engler-Stringer et al., 2014b; Kestens and Daniel, 2010; Kwate and Loh, 2010; Neckerman et al., 2010; Robitaille et al., 2010; Sturm, 2008). There is thus a need to better connect research on the food retail environment and the food information environment. Despite a growing literature on the contributions of food retailers on dietary intake (Williams et al., 2014), to our knowledge no study has systematically examined the relationship between the type and prevalence of retail food outlets and the advertising environments surrounding schools. Finally, while several studies have noted significant differences in the advertisement environments inside schools for elementary versus secondary school students (Johnston et al., 2007; Terry-McElrath et al., 2014; Velazquez et al., 2015b), existing studies of advertising environments around schools have not compared environments by school type. This is a gap considering that advertisements may also have an impact on older students who have greater autonomy and more spending money to act on the messaging from food advertising. Identifying types of neighbourhoods with high advertising exposures, examining the role of retailers as sources of these exposures, and testing for differences in exposures across elementary versus secondary schools could prove important as a variety of stakeholder groups including government agencies and non-governmental organizations seek to regulate food marketing to children (Health Canada, 2017b; World Cancer Research Fund, 2017; World Health Organization, 2010). Thus, our research aimed to describe the prevalence and characteristics of food and beverage advertisements (e.g., posters or other physical materials with branded or non-branded information, images related to food, or logos for provincially or nationally recognizable food or beverage retailers with the intent to relay information and/or increase awareness about a particular food or beverage product) surrounding public elementary and secondary schools in a large Canadian city. We then assessed whether the advertising landscape differed by neighbourhood socio-economic deprivation, school type (elementary versus secondary), and—for advertisements located on store exteriors—store type. 2. Methods Data were collected through ground-truthing, the systematic surveying of neighbourhoods surrounding schools, between June 29 and September 30, 2015. The sample included 26 geographically and socioeconomically diverse schools (20 elementary and 6 secondary) in Vancouver, Canada that had participated in a related study focussed on school food systems and dietary practices (Ahmadi et al., 2014; Velazquez et al., 2015b)). Schools were purposefully chosen for the original study to ensure that schools varied in terms of neighbourhoodlevel SES, food environment characteristics and commercial density. Moreover, The Vancouver Board of Education has six defined geographic sectors and at least one school participated from each sector to capture geographic diversity across the school board region. (Vancouver School Board, 2012). ArcGIS software 10.3.1

1 Authors’ calculations from a survey of 5th − 8th grade students (n = 998) in Vancouver public elementary and secondary schools; see Ahmadi et al. (2014) and Velazquez et al. (2015a) for survey details.

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Fig. 1. Map of advertisement locations within 400 m line-based buffers surrounding surveyed schools.2

modified version of the categories outlined in the Guidelines for Food and Beverage Sales in BC Schools (e.g., fruit and vegetables, grain products, meat and alternatives, milk and alternatives), and the nutritional criteria proposed by these Guidelines which then categorize items as “sell most” (e.g., healthier options providing essential nutrients, lower in sodium, sugar, and fat), “sell sometimes” (e.g., options that provide some essential nutrients but have higher amounts of sodium, sugar, or fat), and “do not sell” (e.g., less healthy options, higher in sodium, sugar, or fat) foods. (BC Ministry of Health and BC Ministry of Education, 2013). The coder also noted the presence of common marketing techniques including animated characters (e.g., cartoons), premium offers (e.g., giveaways, contests), and direct messaging (e.g., explicitly written messages), and whether materials were professionally-made (e.g., designed and printed professionally). See Table 1 for further description and examples of advertisement measures. Geocoded school locations were obtained from the Vancouver Open Data Catalogue (DataBC, 2016). Researchers used the 2015 Vancouver Business Licences (City of Vancouver, 2016) to construct a measure of commercial density, calculated as the count of all retail outlets located within the line-based buffer surrounding each school (Daepp and Black, 2017). Finally, the socio-economic status of the area surrounding each school was assessed with the Vancouver Area Neighbourhood Deprivation Index (VANDIX), a weighted sum of census measures indicative of socio-economic deprivation (Bell et al., 2009a, 2009b). The VANDIX, which has predictive validity for the study of socio-economic gradients in health status across small areas (Bell and Hayes, 2012), was constructed with data from the 2006 Census of Canada. Each school was assigned the VANDIX score of the dissemination area directly surrounding that school, and scores were split into tertiles, with “high” scores assigned to schools in the most deprived tertile and “low” scores indicating schools in the least deprived tertile.

(Environmental Systems Research Institute (ESRI), 2015) was used to construct 800 m line-based buffers surrounding each school. The linebased buffer was created by identifying all street network segments within 750 m of each school and then constructing 50 m buffers around these segments following Oliver et al. (2007); the resulting buffers are shown in Fig. 1. Two surveyors visited each major commercial street and one surveyor visited each residential street within the buffers to identify and photograph all food stores and food-related promotions following a detailed ground-truthing protocol (Daepp and Black, 2017). Food-related promotions comprised both advertisements—defined as posters or other physical materials with branded or non-branded information, images related to food, or logos for provincially or nationally recognizable food or beverage retailers with the intent to relay information and/or increase awareness about a particular food or beverage product—and store signage (e.g., signs identifying names of independently run (non-chain) restaurants). If a promotion was located on or around a food outlet, the location was classified according to store type as (1) limited-service restaurant, (2) convenience store, (3) grocery store, or (4) other (e.g., full service restaurants). Following definitions used in previous research (Clary and Kestens, 2013; Fleischhacker et al., 2012; Han et al., 2012; Lucan et al., 2013), a limited-service restaurant was defined as a food store offering prepared foods where customers primarily order at a counter and pay before eating; convenience stores were food retailers (including gas station marts and drug stores) selling packaged or prepared food; and grocery stores were defined as food retailers with the five departments of a traditional grocer (dairy, bakery, butcher, deli, and produce). Finally, the surveyors recorded addresses and locations of all observed food stores and food-related promotions using a Garmin eTrex 20x Worldwide Handheld GPS Navigator to collect GPS coordinates. Photographs were used for coding and verification purposes using a classification protocol adapted from previous work (Velazquez et al., 2015b). A single researcher reviewed all images and assigned each photograph codes where appropriate. First, photographs were coded according to overall purpose to differentiate advertisements from signage, a practice that has been followed in other work (Kelly et al., 2008), to obtain an overall sense of the information environment to which youth are exposed. Signs were not coded beyond this point and were subsequently excluded from the remaining analyses. All advertisements were further coded according to main purpose (e.g., individual item, branded logo), category of food depicted using a

2 The full population of public schools in the Vancouver School Board are grey triangles, with surveyed schools highlighted in yellow. We omitted one school (orange) with a buffer zone almost completely overlapping that of a neighbouring school. While surveyors collected advertisement and food retailer data within 800 m buffers (purple), final analyses were reported within 400 m buffers to minimize duplication of advertisements across schools. Therefore, only advertisements located within the 400 m buffers around schools are shown; symbols noting advertisement availability were further jittered to show the high density of advertisements along specific streets.

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Table 1 Description and/or example of promotion attributes. Promotion attribute Category Advertisement - Branded Advertisement – NonBranded Branded Logo Food/Menu Picture Signage Store type Limited-service restaurant Grocery store Convenience store Other Main purpose Passive Food/ Beverage Item Single Item Advertisement Branded Logo

Description and/or Example Branded, provincial or nationally recognizable, product information No branded, provincial or nationally recognizable, product information Branded logos for provincial or national products/restaurants Edible food item (stand-alone item with no other words), sandwich board Signs identifying names of independent restaurants A food outlet offering prepared foods where customers primarily order at a counter and pay before eating A food retailer with the five departments of a traditional grocer (dairy, bakery, butcher, deli, and produce) A food retailer (including gas station marts and drug stores) selling packaged or prepared foods Bus stops, free-standing billboards and other locations as well as food vendors not likely to serve schoolchildren (e.g. full-service food restaurants and pubs). Item (e.g., apple) shown without any written message Only one item or type of product depicted Branded logos for przovincial or national recognizable products and/or restaurants

Category: Advertisement – Branded; Main Purpose: Individual Item; Food Group: Other Beverages; Classification: Do Not Sell Other Food groupingsa Candies and Chocolates Condiments Energy Bars Fruit and Vegetables Grain Products Meat and Alternatives Milk and Alternatives Mixed Entrees Other Beverages Water Classification Sell Most Sell Sometimes Do Not Sell

Menu, recipe Mints, cough drops, chocolate bars Ketchup, mustard, mayonnaise Meal replacement bars, sports bars Apple, carrot, fruit juice Rice, pasta, bagels Beef, poultry, eggs Milk, cheese, yogurt Sandwiches, burgers, pizza Soft drinks, energy drinks Bottled or tap water Higher in essential nutrients and lower in sodium, sugar, and fat (e.g., whole grain products, fresh vegetables) Provide essential nutrients but have higher amounts of sodium, sugar, and fat than sell most items (e.g., flavoured yogurts) Higher amounts of fat, sodium, and sugar and may be less nutritious (e.g., regular, full sugar soft drinks)

Category: Advertisement – Branded; Main Purpose: Individual Item; Food Group: Milk and Alternatives; Classification: Do Not Sell a

Food groupings were modified based on the Guidelines for Food and Beverage Sales in BC Schools.

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2.1. Data analysis

but four schools had at least one advertisement within 400 m. While 40% of schools had fewer than ten advertisements within 400 m, 20% had 50 or more advertisements (median = 18, range 0–96). The retail food environment was an important contributor to these advertising exposures: a majority (n = 500, 76.6%) of advertisements were located on retail food outlets including limited-service restaurants (n = 204, 31.2%), convenience stores (n = 274, 42.0%), or grocery stores (n = 22, 3.4%). The remaining advertisements were located on fullservice restaurants6 (n = 72, 11.0%), bus stops (n = 17, 2.6%), and other built environment features. Most advertisements included branding associated with a provincially or nationally recognizable company (n = 485, 74.3%), but the presence of other common marketing strategies like cartoons/celebrity characters (n = 18, 2.8%) and premium offers (n = 64, 9.8%) on advertisements were infrequent. Boxplots depict the counts of advertisements around each school according to food grouping (Fig. 2) and BC School Food Guideline classification type (Fig. 3). The median number of advertisements for most food groupings (candies and chocolates, condiments, energy bars, grain products, meat and alternatives, plain water, and side dishes) within 400 m of schools was zero; however, a majority of schools had at least 1 fruit and vegetable advertisement (range 0–7), 3 advertisements for milk and alternatives (range 0–43), 3 for mixed entrees (range 0–2), and 4 advertisements for other beverages such as soft drinks or energy drinks (range 0–24). As shown in Fig. 3, the median number of advertisements classified as “sell most” was 1 (range 0–8) while the median number classified as “do not sell” was 10 (range 0–52).

First, ArcGIS software 10.3.1 was used to geolocate all advertisements and stores and to constrain the data to 400 m line-based buffers surrounding each school. The 400 m distance reflects an approximately 5-min walking distance (Pikora et al., 2002) and is similar to the distance used by Kelly et al., (2008, 2015)3 The total count of advertisements observed was tabulated according to coded attributes both overall and stratified across levels of neighbourhood socio-economic deprivation (i.e., low, medium, high) or school type (i.e., elementary versus secondary) associated with the 400 m buffer zone within which the advertisement was located. Advertisements located on the exteriors of limited-service food outlets were additionally tabulated according to coded attributes, stratified by store type. Chi-squared tests were used to test whether the distribution of advertisements’ coded attributes differed across levels of neighbourhood socio-economic deprivation, school type, or store type (Table 2). Negative binomial regression analyses were conducted to examine associations between neighbourhood socio-economic deprivation, school type, and store makeup (counts of stores according to store type) with the number and type of advertisements, controlling for overall commercial density within the 400 m line-based buffer surrounding each school. Models shown in Table 3 were first fitted with advertisements as the outcome variable (including branded advertisements, nonbranded advertisements, and food pictures while excluding logos). As a sensitivity analysis, we fitted models for all advertisements (the aforementioned categories as well as logos), and for branded and nonbranded advertisements only (Appendix A). Advertisements were further stratified according to advertisement classification, with separate models fitted for the counts of advertisements around each school that depict “sell most”, “sell sometimes”, and “do not sell” foods. Negative binomial models were used to account for overdispersion in the counts of advertisements around schools.4 Inter-rater reliability, while not a threat to the overall reliability of our results (because a single researcher rated all items in the current study), was evaluated to assess the reproducibility of our coding approach should other researchers be interested in using a similar protocol. As such, a random subset of images (n = 100) was selected, and photograph attributes were independently scored by two researchers. Individual scores for each attribute (all categorical variables) were compared using Cohen's kappa statistic (κ), which takes into account agreement credited to chance alone. Analyses were conducted in R 3.4.4 (R Core Team, 2018) and STATA 14 (StataCorp, 2015).

3.2. Advertisement frequencies by neighbourhood deprivation, school type and store type Table 2 reports the frequencies of advertisements observed within 400 m of schools during ground-truthing. Within 400 m of schools, 335 advertisements were branded advertisements (51.3%), 155 were food pictures (23.7%) and 149 were logos (22.8%). Researchers identified just 14 non-branded advertisements (2.1% of all advertisements). The distributions of observed advertisements in each category differed significantly by neighbourhood socio-economic deprivation (p = 0.002). In comparison with less deprived neighbourhoods, the most deprived neighbourhoods had proportionally more branded advertisements (56.2% versus 41.9%) and food pictures (30.1% versus 22.2%) and fewer logos or non-branded advertisements. The categories of advertisements on store exteriors also differed significantly by store type (p < 0.001), with convenience stores promoting proportionally more branded advertisements (75.5%) in comparison with limited-service food outlets or grocery stores, where branded advertisements accounted for 34.8% and 31.8% of all advertisements, respectively. No significant differences were observed in the categories of advertisements around elementary versus secondary schools (p = 0.404). For all advertisements, the most frequently depicted products were: mixed entrees such as pizza or burgers (20.7%), “other” beverages such as soft drinks or energy drinks (19.4%), and milk and alternatives (19.0%). Though there were 44 advertisements featuring fruits and vegetables, just under half (n = 21) of these advertisements were classified as "sell sometimes" or “do not sell” because many were for items such as fruit juices or smoothies containing added sugars. Overall, just 6.7% of advertisements were for “sell most” items, whereas 24.0% and 45.6% were for “sell sometimes” and “do not sell” items, respectively; the remaining 23.6% of advertisements were logos or similar images that could not be classified. When logos were also excluded, 8.7% of advertisements were for “sell most”, 31.2% were for “sell

3. Results 3.1. Availability and distribution of advertisements We identified 6535 advertisements within 400 m of 25 schools (See Fig. A1 for exclusion criteria). Schools varied in the number of advertisements to which their students were exposed. Fig. 1 shows that all 3 Reporting primary results for 400 m buffers also allows us to minimize the effect of overlapping buffers; however, data were collected within 800 m buffers (the most common buffer distance used in the school food environments literature (Williams et al., 2014) and thus results using 800 m buffers are available from the authors on request. 4 Likelihood ratio tests assessed whether the inclusion of an additional overdispersion parameter in the negative binomial model led to a significant improvement in model fit in comparison with a Poisson model. The test rejected the null hypothesis of no significant improvement in fit for the model with all advertisements and the model with “do not sell” (p < 0.001), failing to reject the null hypothesis for the model with “sell most” (p = 0.056) or “sell sometimes” (p = 0.50). 5 We identified 640 unique advertisements, but thirteen advertisements were located in two school buffer zones simultaneously.

6 We focus on non-full-service outlets in our analyses because (1) children are less likely to frequent full-service restaurants in comparison with limited-service, convenience, or grocery outlets and (2) full-service restaurants disproportionately displayed signage rather than advertising.

5

6

167

70 (41.9) 55 (32.9) 5 (3.0) 37 (22.2)

42 (25.1) 68 (40.7) 2 (1.2) 55 (32.9)

3 (1.8)

3 (1.8) 0 (0.0) 5 (3.0) 12 (7.2) 4 (2.4) 17 (10.2) 32 (19.2) 34 (20.4) 0 (0.0) 0 (0.0) 57 (34.1)

6 (3.6) 39 (23.4) 65 (38.9) 57 (34.1)

653

335 (51.3) 149 (22.8) 14 (2.1) 155 (23.7)

170 (26.0) 330 (50.5) 4 (0.6) 149 (22.8)

22 (3.4)

9 (1.4) 0 (2.0) 13 (2.0) 44 (6.7) 15 (2.3) 124 (19.0) 135 (20.7) 127 (19.4) 9 (1.4) 1 (0.2) 154 (23.6)

44 (6.7) 157 (24.0) 298 (45.6) 154 (23.6)

Low (n = 8)

15 59 89 33

(7.7) (30.1) (45.4) (16.8)

3 (1.5) 0 (0.0) 2 (1.0) 16 (8.2) 7 (3.6) 44 (22.4) 46 (23.5) 35 (17.9) 1 (0.5) 0 (0.0) 33 (16.8)

9 (4.6)

63 (32.1) 101 (51.5) 0 (0.0) 32 (16.3)

102 (52.0) 32 (16.3) 3 (1.5) 59 (30.1)

196

Middle (n = 9)

23 (7.9) 59 (20.3) 144 (49.7) 64 (22.1)

3 (1.0) 0 (0.0) 6 (2.1) 16 (5.5) 4 (1.4) 63 (21.7) 57 (19.7) 58 (20.0) 8 (2.8) 1 (0.3) 64 (22.1)

10 (3.4)

65 (22.4) 161 (55.5) 2 (0.7) 62 (21.4)

163 (56.2) 62 (21.4) 6 (2.1) 59 (20.3)

290

High (n = 8)

0.001**

0.003**

0.001**

0.002**

p-valuec

Neighourhood Socio-economic deprivation

34 (6.6) 123 (23.9) 241 (46.9) 116 (22.6)

6 (1.2) 0 (0.0) 12 (2.3) 34 (6.6) 12 (2.3) 105 (20.4) 105 (20.4) 96 (18.7) 5 (1.0) 1 (0.2) 116 (22.6)

22 (4.3)

137 (26.7) 262 (51.0) 3 (0.6) 112 (21.8)

266 (51.8) 112 (21.8) 13 (2.5) 123 (23.9)

514

Elementary (n = 19)

School type

10 34 57 38

(7.2) (24.5) (41.0) (27.3)

3 (2.2) 0 (0.0) 1 (0.7) 10 (7.2) 3 (2.2) 19 (13.7) 30 (21.6) 31 (22.3) 4 (2.9) 0 (0.0) 38 (27.3)

0 (0.0)

33 (23.7) 68 (48.9) 1 (0.7) 37 (26.6)

69 (49.6) 37 (26.6) 1 (0.7) 32 (23.0)

139

Secondary (n = 6)

0.582

0.0485*

0.660

0.404

p-valuec

17 47 53 87

(8.3) (23.0) (26.0) (42.6)

1 (0.5) 0 (0.0) 6 (2.9) 20 (9.8) 5 (2.5) 3 (1.5) 66 (32.4) 14 (6.9) 0 (0.0) 1 (0.5) 87 (42.6)

1 (0.5)

50 (24.5) 67 (32.8) 4 (2.0) 83 (40.7)

71 (34.8) 83 (40.7) 1 (0.5) 49 (24.0)

204

Limited-Service (n = 104)

Store typeb

10 (3.6) 18 (6.6) 200 (73.0) 46 (16.8)

0 (0.0) 0 (0.0) 2 (0.7) 8 (2.9) 0 (0.0) 109 (39.8) 3 (1.1) 86 (31.4) 7 (2.6) 0 (0.0) 46 (16.8)

13 (4.7)

21 (7.7) 207 (75.5) 0 (0.0) 46 (16.8)

207 (75.5) 46 (16.8) 4 (1.5) 17 (6.2)

274

Convenience Stores (n = 56)

10 (45.5) 6 (27.3) 3 (13.6) 3 (13.6)

1 (4.5) 0 (0.0) 1 (4.5) 10 (45.5) 2 (9.1) 1 (4.5) 1 (4.5) 2 (9.1) 0 (0.0) 0 (0.0) 3 (13.6)

1 (4.5)

13 (59.1) 7 (31.8) 0 (0.0) 2 (9.1)

7 (31.8) 2 (9.1) 2 (9.1) 11 (50.0)

22

Grocery Stores (n = 22)

< 0.001***

< 0.001***

< 0.001***

< 0.001***

p-valuec

* p < 0.05 ** p < 0.01 *** p < 0.001 a N’s (with percentages in parentheses) reflect the total advertisements including duplicates (n = 13, 2% of the total). b Values are calculated only for those advertisements located on or around a retail food outlet. c P-values from chi-squared tests of independence. d B = branded (contains branded, provincially or nationally recognizable, product information, NB = non-branded (does not contain branded, provincial or nationally recognizable, product information). e P-values from Fisher’s exact test with p-values from Monte Carlo simulation. f NI = Not Identifiable; NA = Not Applicable (e.g. for logos).

Total Advertisements Category Advertisement, Bd Logos Advertisement,NBd Food pictures Purposec Passive food signs Individual Item Other Logo Food Groupe Candies and Chocolates Condiments Energy bars Grain products Fruit and vegetables Meat and alternatives Milk and alternatives Mixed Entrée Other Beverage Plain Water Side Dish NI or NAf Food Classificationc Sell Most Sell Sometimes Do Not Sell NI or NAf

Overall

Table 2 Descriptive statisticsa for all advertisements observed within 400 m of schools, presented according to neighbourhood socio-economic deprivation, school type, and, for those advertisements on store exteriors, store type where the advertisement was located.

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7 4.17** [1.59, 10.96]

1.07* [1.01, 1.13] 1.15 [0.98, 1.35] 0.69** [0.55, 0.86]

— 3.20** [1.50, 6.82]

— 2.18* [1.03, 4.60] 1.07 [0.99, 1.15] 1.38*** [1.19, 1.60] 0.91 [0.73, 1.12]

— 2.27 [0.92, 5.61] 1.22 [0.47, 3.13]

— 1.60 [0.72, 3.54] 0.87 [0.31, 2.44]

(2) All Ads (excluding logos)

1.04 [0.90, 1.21] 1.21 [0.89, 1.63] 1.16 [0.86, 1.57]

— 1.16 [0.31, 4.33]

— 3.28 [0.97, 11.11] 3.34* [1.07. 10.42]

(3) Sell most

2.58 [0.51, 13.11]

1.06 [0.93, 1.22] 1.06 [0.75, 1.49] 0.94 [0.62, 1.43]

— 1.57 [0.38, 6.42]

— 4.57* [1.31, 15.94] 4.65** [1.51, 14.33]

(4) Sell most

1.12*** [1.10, 1.14] 1.18*** [1.09, 1.28] 1.09* [1.01, 1.18]

— 1.08 [0.63, 1.86]

— 2.48*** [1.84, 3.35] 0.96 [0.61, 1.51]

(5) Sell Sometimes

1.32 [0.55, 3.16]

1.12*** [1.10, 1.15] 1.14 [1.00, 1.30] 1.03 [0.86, 1.24]

— 1.17 [0.63, 2.18]

— 2.69*** [1.95, 3.70] 1.12 [0.59, 2.12]

(6) Sell Sometimes

1.07 [0.99, 1.16] 1.41*** [1.19, 1.69] 0.95 [0.75, 1.21]

— 2.12 [0.97, 4.64]

— 1.48 [0.60, 3.63] 0.81 [0.26, 2.51]

(7) Do Not sell

4.89** [1.65, 14.52]

1.07* [1.00, 1.13] 1.15 [0.96, 1.39] 0.70** [0.54, 0.89]

— 3.26** [1.45, 7.36]

— 2.24 [0.76, 6.39] 1.20 [0.40, 3.60]

(8) Do Not sell

Incidence Rate Ratios with 95% confidence intervals in brackets, using robust standard errors. * p < 0.05, ** p < 0.01, *** p < 0.001. Each column presents one model with the count of advertisements in each classification (all, sell most, sell sometimes, and do not sell) as the dependent variables. All models include neighbourhood socio-economic deprivation, school type, and store makeup—counts of stores according to store type; models (2), (4), (6) and (8) additionally control for commercial density. b Measured with the standardized VANDIX score of the dissemination area surrounding the school.

a

Commercial density (100 outlets in 400 m)

Secondary Store makeup (count within 400 m) Limited Service Convenience Grocery

School type Elementary

Neighbourhood socio-economic deprivationb Low Middle High

(1) All Ads (excluding logos)

Table 3 Resultsa from multivariable negative binomial regressions modeling number and type of advertisements within 400 m of schools (n = 25) as a function of neighbourhood socio-economic deprivation of the surrounding dissemination area, school type, and store makeup.

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Fig. 4. Mean count10 of advertisements within 400 m buffers of schools stratified by socio-economic deprivation.

Fig. 2. Boxplots7 of advertisement counts within 400 m line-based buffers surrounding Vancouver schools by food grouping (n = 25 schools).

on convenience stores but just 9.1% of grocery store and 6.9% of limited-service outlet advertisements. Table 2 and Fig. 4 show a socio-economic gradient in the counts of advertisements for advertisements classified as “do not sell” such that the eight school neighbourhoods with high levels of socio-economic deprivation had a higher proportion of advertisements classified as “do not sell” (144 out of 290 advertisements or 49.7%) in comparison with the eight least deprived neighbourhoods (65 out of 167 advertisements or 38.9%). However, neighbourhoods with high deprivation scores also had a higher proportion of “sell most” advertisements (n = 23, 7.9%) in comparison with the least deprived neighbourhoods (n = 6, 3.6%). While the most deprived schools had higher counts of advertisements in every classification category, the least deprived schools had proportionally more advertisements classified as “sell sometimes” (23.4% versus 20.3%) and advertisements such as logos that could not be classified according to nutritional guidelines because they did not depict a specific food product (34.1% versus 22.1%). The type of food groups shown in advertisements also differed by store type (Table 2), with nearly half of all advertisements on grocery stores classified as “sell most” (45.5%), whereas the majority of advertisements associated with convenience stores were classified as “do not sell” (73.0%). Distributions of commercial businesses, socioeconomic deprivation, and retail food outlets are presented in Figs. A2-A4.

Fig. 3. Boxplots8 of advertisement counts within 400 m line-based buffers surrounding Vancouver schools (n = 25 schools) by classification (“Sell Most”, “Sell Sometimes”, “Sell Least”).

sometimes” and 59.1% were for “do not sell” items.9 Distributions of advertisements according to category, purpose or classification did not differ significantly between elementary or secondary schools, though the distributions did differ significantly across levels of socio-economic deprivation and across store types (Table 2). Similarly, the types of food groups depicted differed significantly across store types. Whereas 45.5% of advertisements on grocery stores showed fruits and vegetables, only 9.8% and 2.9% for limited-service outlets and convenience stores did so, respectively. Mixed entrees were common (32.4%) for limited-service food outlets but rare on convenience stores (1.1%) or grocery stores (4.5%); and “other” beverages such as soft drinks or energy drinks made up 31.4% of advertisements

3.3. School-level analyses of number and type of advertisements Negative binomial regressions do not support a socio-economic gradient in total advertisement exposures after adjusting for school type or store makeup (Table 3). Overall, there were no significant differences for total advertisements available or in the counts of advertisements classified as “do not sell” in the least versus most deprived neighbourhoods. However, there were significantly more advertisements classified as “sell most” within 400 m of schools in the most deprived neighbourhoods compared with schools in the least deprived neighbourhoods (IRR = 3.34, 95% CI 1.07–10.42) after controlling for school type and store makeup. After controlling for commercial density as well as neighbourhood socio-economic deprivation, and store makeup, school type was significantly associated with advertisement counts such that secondary schools had 3.20 times more advertisements overall in comparison with

7 The horizontal black line reflects the median per-school count of advertisements in each food group. The lower and upper hinges of the boxes correspond to the 1st and 3rd quartiles, and the whiskers extend to the largest value within ± 1.5 times the interquartile range from the hinges. 8 The horizontal black line reflects the median per-school count of advertisements in each food group. The lower and upper hinges of the boxes correspond to the 1st and 3rd quartiles, and the whiskers extend to the largest value within ± 1.5 times the interquartile range from the hinges. 9 The remaining 1% of advertisements that could not be classified were for a contest related to a menu item at a major fast food chain but did not directly include an image of that item.

10 Colors indicate classification based on the BC School Food and Beverage Guidelines (BC Ministry of Health & BC Ministry of Education 2013) and error bars correspond to standard errors.

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elementary schools (95% CI 1.50–6.82). Comparison of models with the counts of advertisements classified as “sell most” or “sell sometimes” versus “do not sell” suggest that these differences are almost entirely due to an increased presence of advertisements classified as “do not sell” (IRR = 3.26, 95% CI 1.45–7.36), as associations between school type and advertisement counts were not statistically significant in models with the counts of advertisements classified as “sell most” or “sell sometimes” as the dependent variable. Finally, the number of advertisements surrounding schools was also associated with the number and type of stores surrounding schools. After controlling for neighbourhood socio-economic deprivation and school type, the presence of an additional convenience store located in the 400 m buffer zone surrounding a school was associated with a 38% increase in advertisements overall (95% CI 1.19–1.60), and an 18% and 41% increase in advertisements classified as “sell sometimes” (95% CI 1.09–1.28) and “do not sell” (95% CI 1.19–1.69), respectively. Though the association between limited service food outlet counts and advertisement counts was robust to the inclusion of a control for commercial density, associations between convenience store counts and advertisement counts were no longer statistically significant after commercial density was included in the models. Furthermore, the presence of an additional limited-service food outlet was associated with a 7% increase in total advertisements (95% CI 1.01–1.13) and a higher number of grocery stores around a school was associated with a lower number of advertisements overall (IRR = 0.69, 95% CI 0.55–0.86) and a lower number of advertisements classified as “do not sell” (IRR = 0.70, 95% CI 0.54–0.89), but only in models controlling for commercial density. Results were robust to the inclusion of logos as well as to the exclusion of food pictures from advertisement counts for branded and nonbranded advertisements (Table A1).

should be used when interpreting these findings given the small number of secondary schools in this sample and the potentially confounding effects of other geographic differences between elementary and secondary schools. Still meaningful differences may exist because older youth typically have more autonomy to leave school grounds and greater access to spending money, likely making them a more strategic group for retailers to target. Although mixed in the literature, there is some evidence that the extent and nature of food advertising around schools varies by neighbourhood socio-economic status (Kelly et al., 2008), which our findings support. This study offers evidence of a socioeconomic gradient in the unadjusted distributions of advertisements according to classification: the proportion of advertisements classified as “do not sell” made up 38.9%, 45.4%, and 49.7% of advertisements around schools in the lowest, middle, and highest level of neighbourhood socio-economic deprivation, respectively (Table 2). However, the association between advertisement count and neighbourhood socio-economic deprivation was not robust to the inclusion of controls for school type or store makeup, supporting the hypothesis that differences in the makeup of the food retail environment in highly deprived versus less deprived neighbourhoods may confound the association between deprivation and advertising exposures (Kelly et al., 2015). This study also examined factors associated with the number and type of food and beverage advertisements on retail food stores in the area immediately surrounding schools, and found that store exteriors are a considerable contributor to children's exposures. For example, after adjusting for commercial density, each additional limited-service outlet within the 400 m buffer was associated with a 7% increase in total advertisements, a 12% increase in advertisements classified as “sell sometimes”, and a 7% increase in advertisements classified as “do not sell”. There is some evidence suggesting that the density of fast food restaurants around schools is associated with children's food purchasing practices (He et al., 2012; Seliske et al., 2013) and dietary intake (Engler-Stringer et al., 2014a). But the methodological quality of these studies has been critiqued (Williams et al., 2014) as not having sufficiently captured the breadth of exposure children face within and surrounding schools that may impact food purchasing and consumption choices. Thus, we believe there is an opportunity to connect the currently disparate food environment and advertising exposure literatures, especially given the growing interest from government agencies and non-governmental organizations to restrict or regulate food marketing to children (Health Canada, 2017b; Heart and Stroke Foundation of Canada, 2017). Study limitations should also be considered. This study aimed to document the extent and nature of outdoor food and beverage advertising, but we did not capture any messaging that youth are directly exposed to inside school buildings or at the point of purchase. Consequently, our findings underestimate total exposure to food-related advertising near schools. Our measure of school neighbourhoods, a 400 m buffer, is at best a proxy for the area of true exposure relevant to children. Research focused on “activity spaces,” the areas children actually move through on their way to and from school (Chaix et al., 2012), might offer a more valid measure of school neighbourhood exposures. Furthermore, our study examined schools in one Canadian city with a particularly dense food retail environment (Daepp and Black, 2017) and thus, results may not generalize to other cities. Finally, we examined a small number of schools (n = 25) including just six secondary schools, limiting our power to detect significant associations especially by school type. Despite these limitations, our ability to conduct direct observations allowed for reliable and objective measures to be obtained, a strength of this study. We sampled 1/6 of public elementary schools and over one third of all public secondary schools from diverse geographic, socio-economic and commercially dense neighbourhoods within the Vancouver School Board (Ahmadi et al., 2014), allowing us to conclude that outdoor food and beverage advertisements, most of which depicted minimally nutritious items

3.4. Inter-rater reliability The coding scheme showed high inter-rater reliability. Across items, percent agreement was over 75% and Cohen's kappa was greater than 0.6—substantial, according to the Landis and Koch scale (Landis and Koch, 1977)—for all but two categories (premium offer visible, kappa = 0.55, and quality of promotion, kappa = 0.45). For food category and classification, the two codes that are used in this paper to stratify results, Cohen's Kappa was 0.94 and 0.89, respectively. The measures of inter-rater reliability for all other items used in this research can be found in Appendix A Table A2. 4. Discussion This study provides new insight about the nature and type of outdoor food and beverage advertisements in a large Canadian city. Findings from this study indicate that, as in other work (Kelly et al., 2008, 2015; Maher et al., 2005), food and beverage advertising around schools is prevalent and primarily depicts minimally nutritious items. For example, within 400 m, median exposure was 18 advertisements per school and 4 schools were exposed to > 25 advertisements for items categorized as “do not sell” by provincial guidelines for the sale of food in schools. Similar to work from Kelly and colleagues conducted in Sydney and Wollongong, Australia (2008) who found that 80% of food advertisements within a 500 m radius of schools were for minimally nutritious items (e.g., soft drinks, ice cream), our findings suggest that after excluding logos, 31% and 59% of advertisements within 400 m of schools were for items discouraged by provincial school nutrition guidelines (described as “sell sometimes” and “do not sell” items, respectively). Findings suggested that both neighbourhood and school features are associated with the number and/or type of advertisements depicted nearby. After controlling for commercial density, secondary schools were associated with more outdoor food and beverage advertisements overall in comparison with elementary schools. However, caution 9

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discouraged to be sold inside of schools, are likely highly prevalent across the City of Vancouver. To our knowledge, no published research has examined outdoor food and beverage advertising in Canada, much less in areas surrounding child-serving institutions such as schools. This study demonstrates the feasibility of monitoring advertisements in a comprehensive way, using a tool that exhibited strong inter-rater reliability. Future work could provide a more in-depth examination of exposure to food and beverage marketing as a whole around schools including the presence of product displays and pricing strategies. Given recent interest in food availability in and around schools, a relevant next step would be to examine the connections between food and beverage marketing in both of these locations and students' dietary preferences and choices. Exposure to outdoor food and beverage advertising is commonplace among students attending Vancouver public elementary and secondary schools, among a sample of schools where the majority of students sampled in a 2012 study reported walking as their main mode of transport to school.11 While some advertisements depicted nutritious food and beverage options, most did not. Thus, students are likely frequently and repeatedly exposed to messaging that is in direct conflict with the nutrition recommendations advocated within guidelines for food sales at school. Emerging policies aiming at restricting the marketing of minimally nutritious foods to children will need to consider the role that outdoor food advertising surrounding schools plays in the broader informational food environment.

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Acknowledgements We thank Koharu Chayama, who assisted with data collection. CV received funding from the Canadian Institutes of Health Research Postdoctoral Fellowship Award (FRN 127338). Funding support also stemmed from the Canadian Institutes of Health Research Operating Grant entitled ‘What Shapes Food Practices on School Days? Examining the Role of the School Food Environment’ (FRN 119577). MIGD was supported by the University of British Columbia Li Tze Fong Memorial Fellowship (Grant No. #4895) and by a National Science Foundation Graduate Research Fellowship (Grant No. 1122374). All opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at doi:10.1016/j.healthplace.2018.12.007. References Adams, J., Ganiti, E., White, M., 2011. Socio-economic differences in outdoor food advertising in a city in Northern England. Public Health Nutr. 14, 945–950. https://doi. org/10.1017/S1368980010003332. Ahmadi, N., Black, J.L., Velazquez, C.E., Chapman, G.E., Veenstra, G., 2014. Associations between socio-economic status and school-day dietary intake in a sample of grade 5-8 students in Vancouver, Canada. Public Health Nutr. 1–10. https://doi.org/10.1017/ S1368980014001499. BC Ministry of Health & BC Ministry of Education, 2013. Guidelines for food and beverage sales in B.C. schools. Last (Accessed 29 April 2018) from 〈https://www2.gov.bc.ca/ assets/gov/education/administration/kindergarten-to-grade-12/healthyschools/ 2015_food_guidelines.pdf〉. Bell, N., Hayes, M.V., 2012. The Vancouver area neighbourhood deprivation index (VANDIX): a census-based tool for assessing small-area variations in health status. Can. J. Public Health/Rev. Can. De. St. Publique S28–S32. Bell, N.J., Schuurman, N., Morad Hameed, S., 2009a. A small-area population analysis of socioeconomic status and incidence of severe burn/fire-related injury in British Columbia, Canada. Burns 35, 1133–1141. https://doi.org/10.1016/j.burns.2009.04.

11 Authors’ calculations from a survey of 5th − 8th grade students (n = 998) in Vancouver public elementary and secondary schools; see Ahmadi et al. (2014) and Velazquez et al. (2015a) for survey details.

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